Empirical investigation of efficient market hypothesis in vietnam stock market

Abstract This research examines the efficiency of Vietnam stock market at weak form level by using daily and weekly observations of market index and eight selected stocks of real estate

MINISTRY OF EDUCATION AND TRAINING

UNIVERSITY OF ECONOMICS HOCHIMINH CITY

LÊ ĐẶNG BÍCH THẢO

EMPIRICAL INVESTIGATION OF EFFICIENT MARKET

HYPOTHESIS IN VIETNAM STOCK MARKET

MASTER THESIS

Ho Chi Minh City 2011

MINISTRY OF EDUCATION AND TRAINING

UNIVERSITY OF ECONOMICS HOCHIMINH CITY

LÊ ĐẶNG BÍCH THẢO

EMPIRICAL INVESTIGATION OF EFFICIENT MARKET

HYPOTHESIS IN VIETNAM STOCK MARKET

MAJOR: BANKING AND FINANCE

MAJOR CODE: 60.31.12

MASTER THESIS

Supervisor: Dr Võ Xuân Vinh

Acknowledgement

I would like to express my heartfelt gratitude and deepest appreciation to my research Supervisor, Dr Vo Xuan Vinh for his precious guidance, share of experience, ceaseless encouragement and highly valuable advice and comments throughout the course of my research

I would like to thank many of my friends in our group from ebanking class, who have been sharing experience during doing research: Ms Nguyen Thi Kim Ngan,

Ms Tran Thuy Huyen, Ms Do Ngoc Anh, Mr Ta Thu Tin, Ms Pham Thi Tuyet Trinh

My special gratitude is extended to all instructors and staff at Faculty of Banking and Finance Postgraduate Faculty, University of Economics HoChiMinh City (UEH) for their support and the valuable knowledge during my study in UEH

Finally, the deepest and most sincere gratitude goes to my parents, my sisters for their love and support Fulfilling this goal would not have been possible without them

Abstract

This research examines the efficiency of Vietnam stock market at weak form level

by using daily and weekly observations of market index and eight selected stocks of real estate and seafood processing companies for the period from 2007 to 2010 Parametric and nonparametric tests including auto correlation test, run test, variance ratio test, regression test, ARCH, GARCH (1,1) have been employed in this study All tests’ results fail to support the hypothesis of weak form efficiency with daily data, even in case, returns are adjusted for thin trading However, with weekly data, results obtained from run test and autocorrelation test do not completely reject hypothesis of weak form efficiency while result given from variance ratio test fully provides evidence against a random walk Besides that, the findings of no clear calendar effect by examining day of week effect also give the evidence that even if the anomalies existed in the sample period, the practitioners who implement strategies to take advantage of anomalous behavior can cause the anomalies to disappear

Keywords: efficient market hypothesis, randomness, calendar effect

Table of contents

Acknowledgement i

Abstract ii

Table of contents iii

List of tables v

Abbreviations vi

1 INTRODUCTION 1

2 LITERATURE REVIEW 5

2.1 The theory of Efficiency Market Hypothesis 5

2.2 Review of Literature on Weak Form Market Efficiency 7

2.2.1.Evidence from developed markets 8

2.2.2 Evidence from developing markets 10

3 DATA AND METHODOLOGY 14

3.1 Data Description 14

3.2 Methodology 17

3.2.1 Auto Correlation Test 17

3.2.2 Run test 19

3.2.3 Variance ratio test 20

3.2.4 Calendar effect 23

3.2.5 Thin trading adjustment 25

3.2.6 Robustness check 26

4 EMPIRICAL RESULT 27

4.1 Autocorrelation Test 27

4.2 Runs test 34

4.3.Variance ratio test 38

4.4 Day of week effects 44

5 CONCLUSION 48

REFERENCES 50

Appendix 56

Table A 1 Summary results of all tests for daily returns in 2007 56

Table A 2 Summary results of all tests for thin trading adjusted daily returns in 2007 56

Table A 3 Summary results of all tests for daily returns in 2008 57

Table A 4 Summary results of all tests for thin trading adjusted daily returns in 2008 57

Table A 5 Summary results of all tests for daily returns in 2009 58

Table A 6 Summary results of all tests for thin trading adjusted daily returns in 2009 58

Table A 7 Summary results of all tests for daily returns in 2010 59

Table A 8 Summary results of all tests for thin trading adjusted daily returns in 2010 59

List of tables Table 3.1 Descriptive statistics of daily return 15 Table 3.2 Descriptive statistics of weekly return 15 Table 4.1 Results of autocorrelation coefficients and Ljung-Box Q statistics for

daily returns 29 Table 4.2 Results of autocorrelation coefficients and Ljung-Box Q statistics for thin

trading adjusted daily returns 31 Table 4.3 Results of autocorrelation coefficients and Ljung-Box Q statistics for

weekly returns 32 Table 4.4 Results of autocorrelation coefficients and Ljung-Box Q statistics for thin

trading adjusted weekly returns 33 Table 4.5 Results of run test for daily price & return 36 Table 4.6 Results of run test for weekly price & return 37 Table 4.7 Variance ratio test results for daily returns under homoscedasticity and

heteroscedasticity 40 Table 4.8 Variance ratio test results for thin trading adjusted daily returns under

homoscedasticity and heteroscedasticity 41 Table 4.9 Variance ratio test results for weekly returns under homoscedasticity and

heteroscedasticity 42 Table 4.10 Variance ratio test results for thin trading adjusted weekly returns under

homoscedasticity and heteroscedasticity 43 Table 4.11 Results of OSL and GARCH (1,1) models for daily returns 46 Table 4.12 Results of OSL and GARCH (1,1) models for thin trading adjusted

daily returns 47

Abbreviations

ABT : Ben tre Aqua product Import And Export Joint Stock Company

AGF : An Giang Fisheries Import and Export Joint Stock Company

ARCH : Autoregressive conditionally heteroscedastic

CII : Ho Chi Minh City Infrastructure Investment Joint Stock Company

GARCH : Generalised Autoregressive Conditional Heteroscedasticity

ITA : Tan Tao Investment Industry Corporation

SJS : Song Da Urban & Industrial Zone Investment and Development Joint Stock Company

TS4 : Seafood Joint Stock Company No 4

1 INTRODUCTION

Efficient Market Hypothesis (EMH) has been a popular topic for empirical research since the introduction of market efficiency theory by Fama (1965) There are many studies examining whether the stock markets in both developed and emerging countries behave in line with the Efficient Market Hypothesis Most of them focused on weak form efficiency, the lowest level of Efficient Market Hypothesis and the results are mixed On the one hand, some studies reject the hypothesis that the stock markets are in the weak form efficiency (Hoque et al., 2007, Abeysekera, 2001b, Lima et al., 2004) On the other hand, some papers provide the evidence that stock markets in some countries are efficient (Chan et al., 1997, Lee, 1992, Worthington et al., 2004)

Although there are many empirical studies devoted to testing for the weak form of Efficient Market Hypothesis in developed and emerging stock markets, there are not many studies examining the weak form of market efficiency in stock returns in Vietnam market The objective of this study is to investigate the existence of weak form of market efficiency in stock returns in Vietnam, and whether there are any anomalies existing in Vietnam stock market The discovery of anomalous patterns

in stock returns can help investors take advantage of continuing to hold and adjust their buying and selling strategies accordingly to increase their returns by timing the market

Since the establishment on 28 July 2000 with the first security trading center in Ho Chi Minh City (hereinafter called Hose) and only two listed companies that are Refrigeration Electrical Engineering Joint Stock Company (REE) and Saigon Cable and Telecommunication Material Joint Stock Company (SACOM), Vietnam stock market has continued to develop successfully by facing all the challenges and difficulties Over ten years of operation, the total number listed companies have increased significantly to 635 companies with a total market capitalization of VND

650.150 billions (Hose VND 523.933 billions, HNX VND121.217billions) The market capitalization to GDP ratio has been increased year by year It goes up from 0.24% in 2000 to 0.37% GDP in 2010 There are 102 securities companies licensed with a total registered capital of VND 31,866 billion (USD 1,528 million) Total trading accounts are about 1,031,000 (including the 15,000 trading stock accounts

of foreign investors), compared to the 2,908 accounts in 2000 The high and rapid growth of Vietnam stock market is, of course, very appealing to domestic and foreign investors

Although Vietnam stock market has developed rapidly and taken liberalization process recently, it still possesses many of features that are characteristics of emerging markets like more information asymmetry, thin trading and weak institutional infrastructure, which all together could cause market inefficiency However, not all of emerging markets are entirely inefficient such as some researchers who find the evidence to support the weak form efficiency in developing countries: Lima et al.(2004) found that Hong Kong and A shares for both the Shanghai, Shenzhen stocks exchanges are in weak form efficiency Dickinson et al.(1994) also provided the evidence that Nairobi Stock Exchange is behave in line with the market efficiency and Moustafa (2004) also supported the weak form Efficiency Market Hypothesis of United Arab Emirates stock market… Hence, considering the theoretical and practical significance, the testable implications and conflicting empirical evidence of random walk hypothesis motivate us to have a fresh look at this issue of weak form efficiency in the context

of an emerging market, namely Vietnam stock market

This study focuses on testing the weak form market efficiency and some anomalies existing in Vietnam stock market To analyze this issue, we require a decomposition

of daily and weekly return of Vnindex and shares in real estate and seafood

and examine whether the successive stock prices or returns are independently and identically distributed Past stock price has no predictive content to forecast future stock price (Fama, 1970) We will then adjust the data for thin (infrequent) trading that is an important characteristic of Vietnam stock market and that could seriously bias the results of empirical studies on market efficiency

The research provides a number of complementary testing procedures for random walk or weak form market efficiency which have been widely used in the literature

We also perform various tests to examine market efficiency in the weak form, which focus on the information conveyed by past price In particular, we use the parametric serial correlation test of independence which measures the relationship

of the current stock return and its value in the previous period We then use run test,

a nonparametric test, which is computed to test the randomness of stock return Furthermore, the variance ratio test which is proposed by Lo and Mackinlay (1988)

is carried out to check whether uncorrelated increments exist in the series, under the assumption of homoscedastic and heteroscedastic random walk Finally, we use the ordinary least standard (OSL), Autoregressive conditionally heteroscedastic (ARCH), Generalised Autoregressive conditional Heteroscedasticity (GARCH(1,1)) models which have been widely employed in the literature to explore calendar anomalies existing in Ho Chi Minh stock market

By using the latest data, more observations and conducting several robustness checks with the same methodology, our findings are consistent with the previous results of Loc (2006) which report that Vietnam stock market is inefficient in the weak form with daily data However, the extent of inefficiency of Vietnam stock market decreases when the weekly observations are employed in our study Moreover, our research also employs the calendar effect which explores the calendar anomalies in Vietnam market The result of calendar effect especially day

of week does not exist in Vietnam stock market during the studied period

Consequentially, this does not support the findings of Loc (2006) that the day of week effect existing in Vietnam stock market as negative Tuesday effect

The first contribution of our research is that this is one of the studies in Vietnam applying new econometrics, new methodology which has been affected the Brooks’ (2008) methodology This study also has take advantages of all models which have been tested in the previous literatures The second contribution of this study is to provide evidence against persistent patterns in anomaly in Vietnam stock market Then, this study also enhances the established literature by providing the most recent analysis of our stock market

The remainder of this study is structured as follows Section two reports the relevant theoretical background to the research and reviews the previous empirical evidences

on weak form efficiency in developed and emerging countries Section three describes the data and methodology Section four presents the empirical research Finally, section five summarizes the results of the study, draws conclusions and provides suggestions for further research

2 LITERATURE REVIEW

2.1 The theory of Efficiency Market Hypothesis

The Efficient Market Hypothesis is a concept of informational efficiency, and refers

to market’s ability to process information into prices The ideas of Efficient Market Hypothesis appear as early as the beginning of twentieth century in the theoretical contribution of Bachelier (1900) who laid the foundation for random walk hypothesis of market efficiency However, it was until the 1960s, Samuelson (1965) has been developed the theoretical framework for the random walk and Fama (1965) finds supportive evidence of the random walk hypothesis that successive price changes are independent The Efficient Market Hypothesis has been emerged from the combination of empirical findings of Fama (1965) and theory of Samuelson (1965)

Fama (1970) summarizes this idea in his classic survey by writing: "A market in which prices always 'fully reflect' available information is called 'efficient'." According to this hypothesis, in an informatively efficient market, price changes must be unforecastable Since news is announced randomly, price must fluctuate randomly Consequently, it states that it is not possible to exploit any information set to predict future price change In his early paper, Noble prize winner Fama (1970) suggests that the tests of efficient markets could be subdivided into three categories: weak form test, semi strong form test and strong form test efficiency and each category dealing with a different type of information

The weak form test is the lowest level of efficiency A capital market is said to satisfy weak form efficiency if the current stock prices fully incorporate the information in past stock prices Hence, trader can not make abnormal returns based

on the predication of past stock prices The semi strong form efficiency indicates

that the current stock prices including all information known to all market participants Hence, this reflects all public available information such as the information on stock splits, annual reports; new security issues… Trader can not get the abnormal returns by analyzing the annual reports or available public information Finally, strong form test of the efficient market theory tests whether private or confidential information is fully reflected in security prices The current prices of stock including all information known to any market participant including the public and private information, this assumption hardly exists in reality, so the strong form of market efficiency is not very likely to hold Hence, no trader would

be able to get abnormal return above the average investor even if he was given new information

Fama (1970) also introduces three models for testing stock market efficiency including: the Expected Return or fair game model, the submartingale model, and the Random Walk model In this study, we only concentrate on the random walk model which is more powerful in support of the EMH than tests of the fair game model and submartingale model The Efficient Market Hypothesis is associated with the idea of a “random walk” The logic of the random walk idea is that if the flow of information is unimpeded and information is immediately reflected in stock prices, then tomorrow’s price changes will reflect only tomorrow’s news and will be independent of the price changes today But news is by definition unpredictable and, thus, resulting price changes must be unpredictable and random Hence, prices fully reflect all known information, and even uninformed investors buying a diversified portfolio at the tableau of prices given by the market will obtain a rate of return as generous as that achieved by the experts However, in an efficient market, price changes must be a response only to new information Since information arrives randomly, share prices must also fluctuate unpredictably The Random Walk model can be stated in the following equation:

1 1

where: Pt+1 : price of share at time t+1;

P t : price of share at time t; 1

t

ε+ : random error with zero mean and finite variance

The equation indicates that the price of a share at time t+1 is equal to the price of a share at time t plus given value that depends on the new information (unpredictable) arriving between time t and t+1 In other words, the change of price εt+1=Pt+1−Pt

is independent of past price changes

2.2 Review of Literature on Weak Form Market Efficiency

There is a large and growing literature concerning the validity of random walk hypothesis with respect to stock markets in both developed and developing countries However, the empirical research produce mixed results Most early studies are supportive weak forms of Efficient Market Hypothesis in developed capital markets Recent studies, however, document that stock market returns are predictable This section provides a review of the literature on the weak form efficiency in both developed and developing countries

Methodologically, testing the weak form efficiency used the random walk model which is widely employed in the preceding literature Practically, several statistical techniques, runs test, unit root test, serial correlation test, and variance ratio test, are commonly used for testing weak form efficiency Specially, the run test is used

in the literature of Fama (1965), Sharma Kennedy (1977), Cooper (1982), Chiat et

al (1983), Wong et al (1984), Yalawar (1988), Ko and Lee (1991), Butler and Malaikah (1992), Abraham (2002), Worthington and Higgs (2004), Squalli (2006); Daraghma et al (2009) Also, the serial correlation test of returns has also been used extensively by Kendell (1953), and Fama (1965), Fama and French (1988), Worthington et al (2004), Squalli (2006) And the unit root test used by David and

MacKinlay (1988), Worthington et al (2004) And the variance ratio test also used

by Dockery and Vergari (1997), Grieb and Reyes (1999); Alam et al (1999); Chang

et al (2000); Cheung et al (2001); Abraham et al (2002); Seddighi et al (2004), Loc (2006), Hafiz et al (2007) In this study we use all tests that mentioned above (Run test, serial correlation, and variance ratio test, regression test, ARCH; GARCH(1,1)) to enhance the findings of this study

2

Evidence from developed markets

The empirical papers in developed markets generally have similar conclusions that support the weak form efficiency Groenewold (1997) conducts weak and semi strong efficiency tests of Australian stock market by using aggregate share price indexes and finds that the results are consistent with the weak form efficiency In addition, Hudson et al (1996) find that the technical trading rules have predictive power but not sufficient to enable excess return in United Kingdom market

Lee (1992) employs variance ratio test to examine whether weekly stock returns of the United States and ten industrialized countries: Australia, Belgium, Canada, France, Italy, Japan, Netherlands, Switzerland, United Kingdom, and Germany follow random walk process for the period from 1967 to 1988 He finds that the random walk model is still appropriate characterization of weekly return series for majority of these countries

Ayadi et al (1994) apply variance ratio test to examine the efficiency hypothesis of Korean Stock exchange for the period from 1984 to 1988 Under the assumption of homoscedasticity, the authors reject the random walk hypothesis However, under the heteroscedasticity, they could not reject the random walk for daily data In addition, they also employ the weekly, monthly, 60 day and 90 day interval data The results also could not reject the random walk hypothesis

Chan et al (1997) examine the weak form and the cross country market efficiency hypothesis of 18 international stock markets, including Australia, Belgium, Canada, Denmark, Finland, France, Germany, India, Italy, Japan, Netherlands, Norway, Pakistan, Spain, Sweden, Switzerland, the United Kingdom, and the United States for the period from 1962 to 1992 They conclude that all stock markets in the sample are individually weak form efficient and only a small number of stock markets show evidence of co-integration with others by using Phillips-Peron (PP) unit root and Johansen’s co-integration tests

C.Cheung et al.(2001) employ variance ratio tests with both homoscedasticity and heteroscedasticity to examine random walk hypothesis for Hang Seng Index on Hong Kong Stock Exchange for period from 1985 to 1997 They conduct that Hang Seng follows a random walk model and consequently that the index is weak form efficient

Worthington et al (2004) investigate random walk in 16 developed markets and four emerging stock markets for the period from 1987 to 2003 By using various methods including serial correlation, runs, three types of unit root test and multiple variance ratio tests, the paper’s result indicates that the random walk hypothesis is not rejected in major European developed markets Particular, Germany and Netherlands are weak form efficient under both serial correlation and runs tests, while Ireland, Portugal and the United Kingdom are efficient under one test or the other Thus, rests of the markets do not follow a random walk The ADF and Phillips-Perron unit root tests reject the null hypothesis of random walk in the all 20 emerging and developed markets, while the KPSS unit root tests fail to reject the null hypothesis excluding the Netherlands, Portugal and Poland Under the variance ratio test, the null hypothesis of homoscedasticity and heteroskedasticity are not rejected in the United Kingdom, Germany, Ireland, Hungary, Portugal and Sweden The rejection of the null hypothesis of the homoscedasticity but not the

heteroscedasticity is found for France, Finland, Netherlands, Norway and Spain Among the emerging markets, only Hungary satisfies the strictest requirements for

a random walk in daily returns

In a more recent research, Kima et al (2008) examine efficiency of stock prices of group Asian markets The weekly, daily data from 1990 are considered in this study By using new multiple variance ratio tests, it is found that the Hong Kong, Japanese, Korean and Taiwanese markets are efficient in the weak form The other markets of Indonesia, Malaysia and Philippines are shown no sign of market efficiency Singapore and Thai markets become efficient after the Asian crisis

2.2.2 Evidence from developing markets

In contrast with the evidence from developed markets, the findings of weak form efficiency on developing markets are mixed Most of developing countries suffer with the problem of thin trading In addition, in smaller markets, it is easier for large traders to manipulate the market Though it is generally believe that the developing countries are less efficient However, the empirical evidence does not always support this thought Many papers report weak form efficiency in developing countries Lima et al (2004) employ data of the daily stock price indexes of Shanghai, Shenzhen (China), Hong Kong, and Singapore Stock exchange over the period from 1992 to 2000 They find that the Hong Kong and A shares for both the Shanghai, Shenzhen stocks exchanges are in weak form efficiency

Dickinson et al (1994) also examine Nairobi Stock Exchange using the autocorrelation and runs tests Their data include weekly prices of the 30 most actively traded stocks from 1979 to 1989 The results also support the weak form of Efficient Market Hypothesis in Nairobi Stock Exchange

Mojustafa (2004) examines the behavior of stock prices in United Arab Emirate market by using the nonparametric runs to test randomness The data consists of daily prices of 43 stocks for the period from 2001 to 2003 The results reveal that 40 stocks out of the 43 are random Hence, this supports the weak form Efficiency Market Hypothesis

In more recent research, Oskooe et al.(2010) examine the random walk hypothesis

in Iran stock market By applying Augmented Dickey Fuller, Philip-Perron, Kwiatkowski, Phillips, Schmidt and Shin and one structural break perron unit root tests for the period from 1999 to 2009 The results from the various unit root tests imply that the Iran daily stock price index follow the random walks process

Many authors, however, argue that markets of the developing countries are in the weak form inefficiency Mobarek et al (2000) study the efficiency of the Bangladesh Security on the Dhaka Stock Exchange by using the autocorrelation, run test for the period of 1988 to 1997 Basing on the result of runs and the autocorrelation tests, the authors argue that the returns of Dhaka stock market do not follow random walks

Abeysekera (2001) indicates that the Colombo Stock Exchange (CSE) in Sri Lanka

is weak form inefficient by using the serial correlation, runs and unit root tests for the period from 1991 to 1996 The findings of three tests consistently reject the random walk hypothesis The author also examines a day of the week and month of the year effect on the CSE, but neither effect found to be on the stock market in Sri Lanka

Smith et al.(2003) examine the random walk hypothesis for five medium size European emerging stock markets by using the multiple variance ratio tests for the period from 1991 to 1998 The findings of Greece, Hungary, Poland, Portugal

markets are fail to support the hypothesis of random walk because the returns are auto correlated In Turkey, however, the Istanbul stock market follows a random walk.

Abrosimova et al (2002) test weak form efficiency in Russian stock market ranging from 1995 to 2001 by employing unit root, autocorrelation and variance ratio tests The results of both autocorrelation and variance ratio tests reject the hypothesis of the random walk for daily and weekly, but not for monthly data For monthly data, the variance ratio under assumption of heteroscedasticity increments the hypothesis

of random walk can not be rejected

Hoque et al (2007) examine the random walk hypothesis for eight emerging equity markets in Asia including Hong Kong, Indonesia, Korea, Malaysia, the Philippines, Singapore, Taiwan and Thailand from 1990 to 2004 The result of variance ratio test indicates that the stock prices of eight Asian countries do not follow the random walk with the exceptions of Taiwan and Korea

Abrim et al.(2009) employ the data of 35 stocks listed in the Palestine Security stock exchange (hereinafter call PSE) to investigate whether the Palestine Security stock exchange is of weak form efficiency by using autocorrelation test, unit root test and run test This paper’s result indicates that the PSE is inefficient at the weak from all test results

The findings from more recent research by Abdmoulah (2010) documents that the stock market in Arab is not weak form efficiency by using the Garch M (1,1) model implemented for 11 Arab stock markets including daily prices of the national indexes of Saudi Arabia, Kuwait, Tunisia, Dubai, Egypt, Qatar, Jordan, AbuDhabi, Bahrain, Morocco and Oman for periods ending in March 2009

Overall, the empirical results from both developed and developing markets show contrasting evidence on weak form efficiency Especially, results of whether or not emerging markets follow a random walk are rather conflicting Mixed results from literature on emerging stock markets efficiency are not surprising since it is observed that emerging stock markets are generally less efficient than developed markets In addition, with the characteristic as high level of liquidity and trading activity, substantial market depth and low information asymmetry, developed markets are seem to be in the weak form efficiency market while most of developing markets are characterized as more information asymmetry, lower volume and frequency of trading (thin trading) and weak institutional infrastructure, settlement delays, weaker disclosure and accounting requirement, which all together could cause market inefficiency (Islam et al., 2005) However, not all of developing markets are necessarily entirely inefficient such as Hong Kong (Lima et al., 2004), Nairobi Stock Exchange (Dickinson et al., 1994), United Arab Emirate (UAE) (Moustafa, 2004), Iran stock market (Oskooe et al., 2010)

Although there are many authors study about the market efficiency for both developed and developing markets However, there are not many researches empirically investigating the market efficiency in Vietnam In lieu of the current literature, Loc et al (2010) employ the weekly price of the market index and the five oldest stocks listed at Ho Chi Minh stock exchange for the period from 2000 to

2004 The result from autocorrelation test, run and variance ratio tests indicate that the Vietnam stock market is inefficiency in the weak form

3 DATA AND METHODOLOGY

3.1 Data Description

The employed data in this study consists of time series (daily and weekly frequency) of Vietnam stock market index and stock price in real estate and seafood processing companies for the period from 2007 to 2010 All data is obtained from electronic database from the website cophieu68.com A total of 996 daily and 202 weekly observations for market index and individual stock are obtained Vnindex is selected as a representative for Vietnam stock market index to be studied in this research The stocks in real estate and seafood processing companies are chosen because stocks in real estate sector are highly sensitive to any change in the economy while the stocks in seafood processing industry are stable and less changeable Hence, some real estate stocks including CII, ITA, SJS, TDH which listed before 2007 to be selected for studying in this literature The oldest seafood processing stocks including ABT, AGF, TS4, FMC also employed in the study as those stocks listed before 2007 at Hose

Where Rt is return at time t, Pt and Pt-1 are price at time t and t-1 respectively

In this study, we follow previous empirical works and employ the most familiar econometrics methods that used in the literature to test the independence of prices data The study applies parametric and non-parametric methods to test the random walk hypothesis In particular, we use the parametric serial correlation test which measures the relationship of the current stock return and its value in the previous period We will then use the run test, a nonparametric test, which is computed to test the randomness of stock return Furthermore, variance ratio test which is proposed by Lo and Mackinlay (1988) will be carried out to check whether

and heteroscedastic random walks Finally, the OSL, ARCH, GARCH(1,1) models have been employed in the literature to explore the calendar anomalies existing in

Ho Chi Minh Stock exchange

Table 3 1 Descriptive statistics of daily returns

Note: ***, ** and * denote a significance level of 1%, 5% and 10% respectively

Table 3 2 Descriptive statistics of weekly returns

Table 3.1 presents a summary of descriptive statistics of the daily returns for Vnindex and eight individual stocks returns Sample means, maximums, minimums, standard deviations, skewness, kurtosis and Jacque-Bera statistics and p-values are reported It can be seen that except TS4 (0.0005), SJS (0.0006), CII (0.0003), all indexes have the negative mean of return The lowest minimum return is in FMC (-0.05856) while the highest maximum return is TS4 (0.04905) The standard deviations of returns range from 0.01939 (Vnindex) to 0.03434 (TS4)

By and large, the statistics shows that the returns of Vnindex and all stocks are not normal distributed Given that the parameters skewness and kurtosis represent the standardised fourth and third moments of a distribution These parameters are used with Jarque-Bera statistics to indicate whether a data set is normally distributed or not Skewness measures the extent to which a distribution is not symmetric about its mean value The skewness of the normal distribution is zero Positive skewness means that the distribution has a long right tail and negative skewness implies that the distribution has a long left tail (Oskooe et al., 2010) Table 3.1 shows that the returns of all stocks except Vnindex, TS4 are positive skewed although the skewness statistics are not large The positive skewness implies that the return of distributions of the shares traded on the exchanges have a long right tail of large values and hence a higher probability of earning positive returns

Moreover, Kurtosis measures the peakness or flatness of the distribution of the series The kurtosis of the normal distribution is three If the kurtosis exceeds three, the distribution is peaked which is indicating as leptokurtic; if the kurtosis is less than three, the distribution is flat, this is indicating as platykurtic The kurtosis value

of all stocks and Vnindex are smaller than three, different from that of a normal distribution, there by indicating the platykurtic frequency distribution of all stocks return series

Finally, the calculated Jarque-Bera statistics and corresponding p-values in table 3.1 are used to test the null hypothesis that the daily distribution of all stock market returns is normally distributed All p-values are smaller than the 0.01 level of significance suggesting the null hypothesis can be rejected Therefore, none of these return series is then well approximated by normal distribution (Chen et al., 2001)

The weekly returns are calculated from the stock prices from Wednesday’s closing price If the following Wednesday price is not available, then the Thursday price (or Tuesday if Thursday is not available) is used If both Tuesday and Thursday prices are not available, the return for that week is reported as missing The choice of Wednesday price aims to avoid the effects of weekend trading and to minimize the number of holidays Table 3.2 presents a summary of descriptive statistics of the weekly returns for Vnindex and eight individual stocks returns By the same analysis with daily return, the weekly returns do not normally distributed

3.2 Methodology

3.2.1 Auto Correlation Test

Autocorrelation test is the most common test which has been used as the first tool for testing of either dependence or independence of random variables The Autocorrelation measures the correlation coefficient between the values of a random variable at time t and its value in the previous period In particular the autocorrelation measures the relationship between the current stock return and its value in the previous period Hence, this test is employed in many empirical studies (Mobarek et al., 2000, Abraham, 2002, Dickinson et al., 1994, Groenewold, 1997, Lima et al., 2004, Islam et al., 2005, Loc et al., 2010) It is calculated as:

1

2 1

N k

t t k t

t t

Where ρk is the serial correlation coefficient of stock returns of lag k; N is the number of observations; rt is the stock return over period t; rt+k is the stock return over period t+k; r is the sample mean of stock returns; and k is the lag of the period

The test aims to examine whether the autocorrelation coefficients are significantly different from zero If the autocorrelation is zero, then the sample of autocorrelations are approximately normally distributed with mean 0 and variance 1/T Then this sample autocorrelation can be used to conduct significance tests for the autocorrelation coefficients in a given confidence interval for an estimated autocorrelation coefficient to determine whether it is significantly different from zero Statistically, the hypothesis of weak form efficiency should be rejected if stock returns (price changes) are successively correlated (ρk is significantly different from zero)

To carry out the examination, this study used the Ljung–Box portmanteau statistic (Q) to test the joint hypothesis that all autocorrelations are simultaneously equal to zero, has been computed as follow:

2 1 ( 2)

k j LB

to three kinds of runs: an upward run (prices go up), a down ward run (prices go down) and a flat run (prices do not change)

The run test can also be designed to count the direction of change from stock returns; for instance, a positive change could be one in which the return is greater than the sample of mean, a negative change one in which the return is less than the mean, and zero change representing a change equal to mean Under the null hypothesis of independence in share price changes (share returns), the actual runs (R) are then counted and compared to the total expected number of runs (m) under the assumption of independence estimated as:

3 2 1

=

=∑ ).For a large number

of observations (N>30), the sampling distribution of m is approximately normal and the standard error of m (σm) is given by:

The standard normal Z-statistics that can be used to test whether the actual number

of runs is consistent with the hypothesis of independence is given by:

0.5 m

share returns when R is too small or too large, the test is a two-tailed one A negative Z value indicates a positive serial correlation, whereas a positive Z value indicates a negative serial correlation The positive serial correlation implies that there is a positive dependence of stock prices, therefore indicating a violation of random walks Since the distribution Z is N (0,1), the critical value of Z at the five percent significance level is ±1.96 Brooks (2008)

3.2.3 Variance ratio test

The variance ratio test which is developed by Lo et al (1988) is not only more powerful but also reliable test of random hypothesis It is designed to test the null hypothesis of random walk process for stock price under the homoscedasticity and heteroscedasticity (Lo et al., 1988) Hence it widely uses by both academics and practitioners to test the market efficiency upon the acceptance of the null hypothesis

(Ayadi et al., 1994, C.Cheung et al., 2001, Kima et al., 2008, Lima et al., 2004, Loc

et al., 2010, Smith et al., 2003, Y.Liu et al., 1991)

The variance ratio test exploits the fact that if the logarithm of price series follows a random walk, then the return variance should be proportional to the return horizon That is the test is based on the assumption that the variance of increments in the random walk series is linear in the sample interval Particularly, if a return series follows a random walk process, the variance of its q differences would be q times the variance of its first differences (He, June 1991)

1 ( t t q ) ( t t )

Where q is any positive integer The variance ratio VR(q) is then determined as follows:

2 2 1

1

( ) ( )

VR q

Var p p

σσ

2 2

( 1)

t t

nq t

p pnq

µσ

Here nq is the number of observation and∅( )q is the asymptotic variance of the

variance ratio under the assumption of homoscedascity

Since finance time series often possess time varying volatilities and deviate from normality Hence, besides the homoscedasticity, this study also uses the Lo et al.’s (1988) heteroscedasiticity robust standard normal test statistics The heteroscedasticity consistent standard normal test statistic, Z*(q) is then defined

Where ∅* ( )q is the asymptotic variance of the variance ratio under the

assumption of heteroscedasticity: And δ∧( )j is the heteroscedasticity – consistent

estimator and computed as follows:

1

2 2 1 1

of the random walk hypothesis have been related with calendar turning points such

as weekend which is significant effects of the calendar affect

This study has been conducted to test the market efficiency in Hose by examining day of week effect present in Vnindex and eight selected stocks of real estate and seafood processing companies To carry out this estimation, we use regression model, ARCH and GARCH (1,1) which have been employed in many empirical research (Abeysekera, 2001a, Hau, 2010, Loc, 2006, Gao et al., 2005, Solnik et al., 1990)

Initially, the ordinary least standard (OSL) has been tested with the dummy variable for Tuesday, Wednesday, Thursday and Friday in the day of week effect The regression can be running under the below model:

regression equation corresponding to the k dummy variables, c is the coefficient of Monday; u t

is an error term and assumed to be independently and identically distributed; and

2 t

σ represents the return variance The OSL is running under the assumption the errors is constant (homoscedasticity)

However, the homoscedasticity assumption of OLS is likely to be violated in the context of financial time series, that is, stock returns If the assumption is not satisfied, the standard errors could be wrong, and, therefore, conclusions inferred from the model could be misleading (Brooks, 2008) Therefore, we will then use the Autoregressive Conditionally Heteroscedastic model to test the ARCH effect whether present in the residuals of an estimate model

We will then use the Generalised Autoregressive Conditional Heteroscedasticity (GARCH) model to test the estimation The GARCH models provide a more flexible framework for capturing the time varying volatility in the return series In

this present study, The GARCH (1,1) model has been employed for testing these effects

4

2 1

Where ω is constant,δ , γ are constants to be estimated

If there is no day of week effect in mean returns, the coefficients are not significantly different from zero The day of week effect is also examined by checking significance of the coefficients c and αk fromα1toα4 The existence of

calendar effect will be accepted when coefficient of at least one dummy variable is statistically significant (Brooks, 2004) The presence of calendar effect in returns will indicate that stock returns in Vietnam are not completely random

3.2.5 Thin trading adjustment

The efficiency of a particular market is dependent upon certain conditions, particularly, the volume of trading Market thinness (low volume of trading) makes

it difficult for traders to react to new information (and therefore prices to reflect it) This can be explained that the absence of a price change or volume of trading between two moments may be interpreted as being caused by the absence of a price reaction to new information and consequently as assign that the market is inefficient In addition, thin trading and illiquidity are features found mostly in emerging markets and prevent trades from being carried out at price shown in the data Antoniou et al (1997) report that observed dependence is not necessarily, but rather may be statistical illusion brought about by thin trading Therefore, test statistics which ignore trading behaviour may be unreliable Many studies have pointed out that thin (or infrequent) trading can seriously bias the results of